研究人员开发了 KQFuzz，一种利用代码库知识引导的大语言模型（LLM）进行量子库测试的新方法。该方法旨在提高基于 LLM 的模糊测试的灵活性和效率，而这在以前是有限的。KQFuzz 采用了一种新颖的提示方案，该方案结合了量子程序代码库的知识来生成高质量的种子程序。该系统还包括评估和变异策略，以提高测试用例的多样性和模糊测试效率。当应用于 Qiskit、PennyLane 和 Cirq 时，KQFuzz 证明了覆盖率的显著提高，最高可达 18.44%，并帮助识别了 13 个已确认的 bug，其中 12 个已修复。 AI
影响 通过提高 bug 检测效率，增强了量子计算库的可靠性。
排序理由 该集群描述了一篇详细介绍量子库测试新方法的最新研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2607.25647v1 Announce Type: cross Abstract: As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical. To this end, many LLM-based fuzzing approaches towards quantum libraries have been proposed…
As quantum computing continually improves, ensuring the reliability and correctness of quantum libraries has become increasingly critical. To this end, many LLM-based fuzzing approaches towards quantum libraries have been proposed to uncover potential bugs. However, these methods…